Official implementation of paper TrajTok: What makes for a good trajectory tokenizer in behavior generation?. Zhiyuan Zhang, Xiaosong Jia, Guanyu Chen, Qifeng Li, Zuxuan Wu, Yu-Gang Jiang, Junchi Yan. ICLR 2026

First Place of Waymo Open Sim Agents Challenge 2025 🏆

Fast WOSAC Metric 🚀

It is very slow to compute the WOSAC metrics with the official code. It usually takes 10-30 seconds to evaluate a single scenario and 100+ hours on the whole validation set. (here are issues)

For fast evaluation and quick development, we developed the fast WOSAC metric that reduces the time to about 0.4s/scenario while maintain the error of each indicator from the official less than $10^{-6}$. The tool supports both 2024 and 2025 version of WOSAC metric and is easy to use in both online and offline modes.

Please refer the document for more information.

Environment Setup

conda create -y -n trajtok python=3.11.9
conda activate trajtok
conda install -y -c conda-forge ffmpeg=4.3.2
pip install -r requirements.txt
pip install torch_geometric
pip install torch_scatter torch_cluster -f https://data.pyg.org/whl/torch-2.4.0+cu121.html
pip install --no-deps waymo-open-dataset-tf-2-12-0==1.6.4

Data Preparation

Step1. Download Waymo Open Motion Dataset v1.3.0.

Step2. run data preprocess script.

# set INPUT_DIR as your dataset path (.../scenario/) before running
bash data_preprocess.sh

Train

python run.py experiment=train task_name=train

Evaluation

python run.py experiment=inference task_name=eval

Tokenization

python -m src.smart.tokens.trajtok

Citation

@inproceedings{zhang2026trajtok,
  title={TrajTok: What makes for a good trajectory tokenizer in behavior generation?},
  author={Zhiyuan Zhang and Xiaosong Jia and Guanyu Chen and Qifeng Li and Zuxuan Wu and Yu-Gang Jiang and Junchi Yan},
  booktitle={International Conference on Learning Representations (ICLR)},
  year={2026}
}

Acknowledgement

Thansk for these excellent opensource works and models: SMART CatK.